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Recovering Individual’s Commute Routes Based on Mobile Phone Data
9 references·2 citing·43 second hop
Recovering Individual’s Commute Routes Based on Mobile Phone Data (2017)Recovering Individual’s Commute Routes Ba…Uncovering urban human mobility from large scale taxi GPS data (2015)Uncovering urban human mobili…Predicting poverty and wealth from mobile phone metadata (2015)Predicting poverty and wealth…Methodologies for Cross-Domain Data Fusion: An Overview (2015)Methodologies for Cross-Domai…Measures of Human Mobility Using Mobile Phone Records Enhanced with GIS Data (2015)Measures of Human Mobility Us…A Simulation-based Approach for Constructing All-day Travel Chains from Mobile Phone Data (2015)A Simulation-based Approach f…From traces to trajectories: How well can we guess activity locations from mobile phone traces? (2014)From traces to trajectories: …Exploring the mobility of mobile phone users (2012)Exploring the mobility of mob…Identifying Important Places in People’s Lives from Cellular Network Data (2011)Identifying Important Places …Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones (2010)Using Mobile Positioning Data…Analyzing detour behavior of metro passengers based on mobile phone data (2022)Analyzing detour behavior of …Methods for Travel Pattern Analysis Using Large-Scale Passive Data (2021)Methods for Travel Pattern An…Using GPS to learn significant locations and predict movement across multiple users (2003)Understanding individual human mobility patterns (2008)Limits of Predictability in Human Mobility (2010)The Mobile Data Challenge: Big Data for Mobile Computing Research (2012)Measuring Large-Scale Social Networks with High Resolution (2014)Urban Sensing Using Mobile Phone Network Data: A Survey of Research (2014)Investigating Causal Relations by Econometric Models and Cross-spectral Methods (1969)Data fusion (2009)Discovering regions of different functions in a city using human mobility and POIs (2012)Prediction of crime occurrence from multi-modal data using deep learning (2017)Multi-source heterogeneous data fusion (2018)Research on user-generated photos in tourism and hospitality: A systematic review and way forward (2023)Migration unemployment and development: a two-sector analysis. 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(1978)The New Mobilities Paradigm (2006)Beyond Space (As We Knew It): Toward Temporally Integrated Geographies of Segregation, Health, and Accessibility (2013)On data processing required to derive mobility patterns from passively-generated mobile phone data (2017)The scaling laws of human travel (2006)Human mobility and socioeconomic status: Analysis of Singapore and Boston (2018)Spatiotemporal distribution characteristics and mechanism analysis of urban population density: A case of Xi'an, Shaanxi, China (2018)Estimating wealth effects without expenditure data—or tears: An application to educational enrollments in states of India (2001)The promises of big data and small data for travel behavior (aka human mobility) analysis (2016)Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States (2017)Economic Growth and Income Inequality (2019)Social physics (2022)Understanding individual mobility patterns from urban sensing data: A mobile phone trace example (2012)A survey of results on mobile phone datasets analysis (2015)Origin–destination trips by purpose and time of day inferred from mobile phone data (2015)Estimating Origin-Destination Flows Using Mobile Phone Location Data (2011)Modelling Transport (2011)Development of origin–destination matrices using mobile phone call data (2014)Passenger Demand And Patterns Of Tourists’ Mobility In The Aegean Archipelago With Combined Use Of Big Datasets From Mobile Phones And Statistical Data From Ports And Airports (2017)Mobile phone data statistics as a dynamic proxy indicator in assessing regional economic activity and human commuting patterns (2020)Vehicle travel path recognition in urban dense road network environments by using mobile phone data (2021)A data driven method for OD matrix estimation (2019)The impact of COVID-19 pandemic on public transport usage and route choice: Evidences from a long-term tracking study in urban area (2021)Exploring Human Mobility Patterns in Urban Scenarios: A Trajectory Data Perspective (2018)Spatial-temporal distribution of carbon emissions by daily travel and its response to urban form: A case study of Hangzhou, China (2020)
this work references cited by second hop

References

  • 2015Predicting poverty and wealth from mobile phone metadata700 cited
  • 2015Methodologies for Cross-Domain Data Fusion: An Overview454 cited
  • 2011Identifying Important Places in People’s Lives from Cellular Network Data401 cited
  • 2010Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones393 cited
  • 2015Uncovering urban human mobility from large scale taxi GPS data280 cited
  • 2012Exploring the mobility of mobile phone users239 cited
  • 2014From traces to trajectories: How well can we guess activity locations from mobile phone traces?164 cited
  • 2015Measures of Human Mobility Using Mobile Phone Records Enhanced with GIS Data112 cited
  • 2015A Simulation-based Approach for Constructing All-day Travel Chains from Mobile Phone Data20 cited

Cited by

  • 2022Analyzing detour behavior of metro passengers based on mobile phone data4 cited
  • 2021Methods for Travel Pattern Analysis Using Large-Scale Passive Data3 cited

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